{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "%reload_ext autoreload\n",
    "%autoreload 2\n",
    "from IPython.core.interactiveshell import InteractiveShell\n",
    "InteractiveShell.ast_node_interactivity = 'all'\n",
    "import sys\n",
    "sys.path.append('../')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "model path =  e:\\Python\\pytorch\\day-ahead-schedule\\evaluate\\..\\config\\..\\result\\msac.pth\n"
     ]
    }
   ],
   "source": [
    "from evaluate.evaluation import load_saved_model\n",
    "from utils.misc import split_action\n",
    "from config.cfg import Config\n",
    "args = Config()\n",
    "print(\"model path = \", args.model_path)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "agent = load_saved_model(args)\n",
    "from environment.ElectricScheduleEnv import env\n",
    "\n",
    "from matplotlib import pyplot as plt\n",
    "\n",
    "plt.rcParams['font.sans-serif'] = ['SimHei']  # 显示中文\n",
    "plt.rcParams['axes.unicode_minus'] = False\t\t# 显示负号\n",
    "\n",
    "days = range(24)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'A区负荷-出力曲线')"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'B区负荷-出力曲线')"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "time at 0 step here 7\n",
      "time at 1 step here 11\n",
      "time at 2 step here 7\n",
      "time at 3 step here 11\n",
      "time at 4 step here 11\n",
      "time at 5 step here 11\n",
      "time at 6 step here 8\n",
      "time at 7 step here 11\n",
      "time at 8 step here 11\n",
      "time at 9 step here 11\n",
      "time at 10 step here 11\n",
      "time at 11 step here 4\n",
      "time at 12 step here 6\n",
      "time at 13 step here 1\n",
      "time at 13 step here 2\n",
      "time at 14 step here 1\n",
      "time at 14 step here 2\n",
      "time at 15 step here 1\n",
      "time at 15 step here 2\n",
      "time at 16 step here 1\n",
      "time at 17 step here 7\n",
      "time at 18 step here 11\n",
      "time at 19 step here 8\n",
      "time at 20 step here 11\n",
      "time at 21 step here 8\n",
      "time at 22 step here 7\n",
      "time at 23 step here 8\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x168554f3a00>]"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x168559a5970>]"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x168554f3ac0>"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x600 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, (axA, axB)= plt.subplots(1,2)\n",
    "fig.set_figheight(6)\n",
    "fig.set_figwidth(8)\n",
    "axA.set_title(\"A区负荷-出力曲线\")\n",
    "axB.set_title(\"B区负荷-出力曲线\")\n",
    "\n",
    "import numpy as np\n",
    "\n",
    "from utils import misc\n",
    "\n",
    "state = env.reset()\n",
    "for i in range(24):\n",
    "    actions = agent.take_target_actions(state)\n",
    "    actionA, actionB = split_action(\n",
    "        actions,\n",
    "        actions_dims_dict={\n",
    "            'fire': [env.A.FireStationsSize, env.B.FireStationsSize],\n",
    "            \"storage\": [env.A.StorageStationsSize, env.B.StorageStationsSize],\n",
    "            \"total\": env.action_dim\n",
    "        },\n",
    "        ts=i,\n",
    "    )\n",
    "    a2b = actionA['exchange']\n",
    "    b2a = actionB['exchange']\n",
    "    # print(f\"fire={actionA['fire']}\\tbuy={actionA['buy']}\")\n",
    "    misc.draw_policy(ax=axA, index=i, actions=actionA, area=0, exchange=a2b)\n",
    "    misc.draw_policy(ax=axB, index=i, actions=actionB, area=1, exchange=b2a)\n",
    "    next_state, r, d = env.step(actionA, actionB)\n",
    "    state = next_state\n",
    "axA.plot(days, env.A.Load[:24], color=\"r\", label=\"负荷\")\n",
    "axB.plot(days, env.B.Load[:24], color=\"r\", label=\"负荷\")\n",
    "\n",
    "# axA.legend(labels=[\"负荷\", \"储能\", \"联络线\", \"购电\", \"火电\", \"太阳能\", \"风能\"])\n",
    "# axB.legend(labels=[\"负荷\", \"储能\", \"联络线\", \"购电\", \"火电\", \"太阳能\", \"风能\"])\n",
    "plt.legend(labels=[\"负荷\", \"储能\", \"联络线\", \"购电\", \"火电\", \"太阳能\", \"风能\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "max fire output, A=3750\tB=27652\n",
      "max load, A=11927.13\tB=20303.635\n"
     ]
    }
   ],
   "source": [
    "print(f\"max fire output, A={env.A.FireMaxOutput}\\tB={env.B.FireMaxOutput}\")\n",
    "print(f\"max load, A={env.A.max_load}\\tB={env.B.max_load}\")"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "pytorch",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.9.10"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}
